शुरू करेंमुफ़्त में शुरू करें

Evaluate the UMAP decision tree model

In the previous exercise, you created a workflow to apply UMAP and build a decision tree model. Now it's time to fit a model using that training data and compare its performance to the unreduced decision tree model. Because the target variable credit_score is categorical, you will use f_meas() to evaluate the models' performances. The unreduced model and its test predictions are stored in dt_fit and predict_df, respectively. The UMAP workflow you created is in umap_dt_workflow. The train and test sets are also provided for you.

The tidyverse, tidymodels, and embed packages have been loaded for you.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Dimensionality Reduction in R

पाठ्यक्रम देखें

अभ्यास निर्देश

  • Use f_meas to evaluate the performance of the unreduced dt_fit.
  • Fit the UMAP reduced model using umap_dt_workflow.
  • Create the test set prediction data frame for the reduced UMAP model.
  • Use f_meas to evaluate the performance of the reduced umap_dt_fit.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

# Evaluate the unreduced decision tree model performance
___(___, ___, ___)

# Fit the UMAP decision tree model
umap_dt_fit <- ___ %>% 
  fit(___ = ___)

# Create test set prediction data frame for the UMAP model
predict_umap_df <- ___ %>% 
  ___(predict = ___(___, ___))

# Calculate F1 performance of the UMAP model
___(___, ___, ___)
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